Intelligent system for the diagnosis of heart disease using data mining and fuzzy modeling

The purpose of this research is to design an intelligent system for diagnosis of heart disease using data mining and fuzzy modeling. The research has been carried out on using the heart disease dataset given by the University of California Machine Learning repository.Master of Science (Biomedical Engineering

ACCOUNTING FOR NON-IDENTICAL DATA IN BIOIMAGE INFORMATICS

Ph.DDOCTOR OF PHILOSOPHY (FOS

Beta-Hemolytic Bacteria Selectively Trigger Liposome Lysis, Enabling Rapid and Accurate Pathogen Detection

For more than a century, blood agar plates have been the only test for beta-hemolysis. Although blood agar cultures are highly predictive for bacterial pathogens, they are too slow to yield actionable information. Here, we show that beta-hemolytic pathogens are able to lyse and release fluorophores encapsulated in sterically stabilized liposomes whereas alpha and gamma-hemolytic bacteria have no effect. By analyzing fluorescence kinetics, beta-hemolytic colonies cultured on agar could be distinguished in real time with 100% accuracy within 6 h. Additionally, end point analysis based on fluorescence intensity and machine-extracted textural features could discriminate between beta-hemolytic and cocultured control colonies with 99% accuracy. In broth cultures, beta-hemolytic bacteria were detectable in under an hour while control bacteria remained negative even the next day. This strategy, called beta-hemolysis triggered-release assay (BETA) has the potential to enable the same-day detection of beta-hemolysis with single-cell sensitivity and high accuracy